We present a novel technique that produces two-dimensional lowdiscrepancy (LD) blue noise point sets for sampling. Using onedimensional binary van der Corput sequences, we construct twodimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.
@article{Ahmed2016LowdiscrepancyBlue, acmid = {2980218}, address = {New York, NY, USA}, articleno = {247}, author = {A. Ahmed, H. Perrier, D. Coeurjolly, V. Ostromoukhov, J. Guo, D. Yan, H. Huang, O. Deussen}, doi = {10.1145/2980179.2980218}, issn = {0730-0301}, issue_date = {November 2016}, journal = {ACM Transactions on Graphics}, keywords = {blue noise, low discrepancy, monte carlo, quasi-monte carlo, sampling}, month = {nov}, number = {6}, numpages = {13}, pages = {247:1--247:13}, publisher = {ACM}, title = {Low-discrepancy Blue Noise Sampling}, url = {http://graphics.uni-konstanz.de/publikationen/Ahmed2016LowdiscrepancyBlue}, volume = {35}, year = {2016} }